Physically-based, distributed hydrologic model for Makkah watershed using GPM satellite rainfall and ground rainfall stations

Al-Areeq, Ahmed M.; Al-Zahrani, Muhammad A.; Sharif, Hatim O.

doi:10.1080/19475705.2021.1924873

Cited by 14 publications

(4 citation statements)

References 55 publications

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“…The predominant land use class in the area is rangeland, followed by bare land and built-up areas. Some land use classes, such as agricultural lands, contain a significantly higher amounts of water than built areas and bare land [45,46]. The LU agricultural land classes were given a high weight owing to their high water content.…”

Section: Land Use (Lu)mentioning

confidence: 99%

Mapping Groundwater Potential Zones in the Habawnah Basin of Southern Saudi Arabia: An AHP- and GIS-based Approach

Ghanim,

Al-Areeq,

Benaafi

et al. 2023

Sustainability

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The excessive depletion of groundwater resources and significant climate change have exerted immense pressure on global groundwater reserves. Owing to the rising global demand for drinking water, as well as its use in agriculture and industry, there is an increasing need to evaluate the capacity and effectiveness of underground water reservoirs (aquifers). Recently, GIS has gained significant attention for groundwater exploration because of its ability to provide rapid and comprehensive information about resources for further development. This study aims to assess and map the groundwater potential of a large basin located in the southern region of Saudi Arabia. Techniques such as GIS and AHP were employed in this study. To perform the delineation for the groundwater potential zones (GWPZ), seven thematic layers were prepared and analyzed. These layers include geology, slope, land use, lineament densities, soil characteristics, drainage density, and rainfall. These variables were carefully considered and examined to identify and categorize areas based on their respective groundwater potentials. The assigned weights to each class in the thematic maps were determined using the well-known analytic hierarchy process (AHP) method. This methodology considered the characteristics of each class and their capacity to influence water potential. The results’ precision was verified by cross-referencing it with existing information about the area’s potential for groundwater. The resulting GWPZ map was classified into the following five categories: very low, low, moderate, high, and very high. The study revealed that approximately 42.56% of the basin is classified as having a high GWPZ. The low and moderate potential zones cover 36.12% and 19.55% of the area, respectively. Very low and very high potential zones were found only in a limited number of areas within the basin. This study holds global importance as it addresses the pressing challenge of depleting groundwater resources. With rising demands for drinking water, agriculture, and industry worldwide, the effective evaluation and management of underground water reservoirs are crucial. By utilizing GIS and AHP techniques, this study provides a valuable assessment and the mapping of groundwater potential in a large basin in southern Saudi Arabia. Its findings and methodology can serve as a model for other regions, supporting sustainable water resource management globally.

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Section: Land Use (Lu)mentioning

confidence: 99%

Mapping Groundwater Potential Zones in the Habawnah Basin of Southern Saudi Arabia: An AHP- and GIS-based Approach

Ghanim,

Al-Areeq,

Benaafi

et al. 2023

Sustainability

Self Cite

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“…Also, the flood velocity and flood depths at any location inside the catchment can be easily determined. GSSHA model results provided detailed information about residential areas and roads that were inundated during flood events [15].…”

Section: Comparison Among Modelsmentioning

confidence: 99%

Development and Application of an Interactive Coupling Rainfall-Runoff Model According to Soil Texture

Kwak

Kim

2021

Water

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In this study, we developed a model that simulates surface and subsurface flows for rainfall-runoff analyses using an interactive coupling method. To characterize the interaction between the surface and subsurface flows, we studied the coupling analysis model. The proposed model was designed following comparisons with existing models. For the analysis of the surface and subsurface flows, a governance equation was constructed. The goodness of fit of the model was also tested. To examine the sensitivity of the input parameters, simulations were performed while changing the major parameters of the model according to the soil texture. The developed model showed high applicability to actual watersheds after adjustment of the parameters. This model can be applied to the extension module for channel analysis; therefore, it can be used efficiently in urbanized watersheds wherein the upstream and downstream parts are pervious and impervious watersheds, respectively.

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“…It detects the flood rate by establishing a relationship between rainfall with runoff. However, it uses field observation data to acquire more accuracy [8]. The primarily used rainfall runoff models include the Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model, and Soil and Water Assessment Tool (SWAT) [9].…”

Section: Introductionmentioning

confidence: 99%

An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques

Ghanim

Shaf

Ali

et al. 2023

Water

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The city of Jeddah experienced a severe flood in 2020, resulting in loss of life and damage to property. In such scenarios, a flood forecasting model can play a crucial role in predicting flood events and minimizing their impact on communities. The proposed study aims to evaluate the performance of machine learning algorithms in predicting floods and non-flood regions, including Gradient Boosting, Extreme Gradient Boosting, AdaBoosting Gradient, Random Forest, and the Light Gradient Boosting Machine, using the dataset from Jeddah City, Saudi Arabia. This study identified fourteen continuous parameters and various classification variables to assess the correlation between these variables and flooding incidents in the analyzed region. The performance of the proposed algorithms was measured using classification matrices and regression matrices. The highest accuracy (86%) was achieved by the Random Forest classifier, and the lowest error rate (0.06) was found with the Gradient Boosting regressor machine. The performance of other algorithms was also exceptional compared to existing literature. The results of the study suggest that the application of these machine learning algorithms can significantly enhance flood prediction accuracy, enabling various industries and sectors to make more informed decisions.

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Physically-based, distributed hydrologic model for Makkah watershed using GPM satellite rainfall and ground rainfall stations

Cited by 14 publications

References 55 publications

Mapping Groundwater Potential Zones in the Habawnah Basin of Southern Saudi Arabia: An AHP- and GIS-based Approach

Mapping Groundwater Potential Zones in the Habawnah Basin of Southern Saudi Arabia: An AHP- and GIS-based Approach

Development and Application of an Interactive Coupling Rainfall-Runoff Model According to Soil Texture

An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques

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